Prediction of porosity in metal-based additive manufacturing using spatial Gaussian process models

By Gustavo Tapia, Alaa H. Elwany, Huiyan Sang. 2016

250 citations recorded by the source corpus.

Subfields: Automotive Engineering, Industrial and Manufacturing Engineering, Mechanical Engineering

View Paper: https://www.semanticscholar.org/paper/30b51bb7460b7eaaa4d11f645d40b40c85f79d5b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Automotive Engineering

1. W. Wong
2. J. Tan
3. Kenneth W. Dalgarno

In Mechanical Engineering

1. W. Wong
2. J. Tan
3. Kenneth W. Dalgarno

Citations in the corpus

• An overview of powder granulometry on feedstock and part performance in the selective laser melting process (2017, background): “Apart from applications which require porous materials, SLM components are otherwise preferred to achieve high part density (>95%) as residual porosity often result in the degradation of mechanical properties and overall part performance [147].”